Problem statement

The Colorado State Forest Service (CSFS) encourages wildfire mitigation through the Forest Restoration & Wildfire Risk Mitigation (FRWRM) Grant Program. The legislation authorizing funding for the program seeks to encourage funding to areas with “fewer economic resources”. The CSFS needs to define “fewer economic resources”, and provide this information to applicants, preferably through the existing Colorado Forest Atlas.

Defining “fewer economic resources” is challenging because resources refer to everything at a community’s disposal. In this work, we will draw from the literature to define an index that captures populations’ vulnerability to natural disasters such as wildland fire. We tailor the index, the Wildland Fire Social Vulnerability Index (WFSVI), to the wildland urban interface (WUI) in Colorado. The WFSVI allows us to identify the areas of the state that are the most vulnerable. We assign the top quartile as eligible for increased match as part of the FRWRM program.

Fewer Economic Resources and Social Vulnerability

The FRWRM grant recognizes the importance of mitigation to reduce the risk to homeowners in the WUI. At a household level, mitigation is usually undertaken privately or with the help of grants, such as FRWRM. In either case, the majority of the burden of mitigation falls on the homeowner. One might expect the widespread adoption of risk-reducing practices as they reduce the risk of potentially catastrophic events. However, Gaither et al. (2011) found that 40% of households in the South reported taking no such action. One reason that households might choose not to mitigate is that they lack the resources to do so. These households may lack financial, educational, and time resources to reduce their risk from wildfire. The inability to reduce risk is correlated with factors that would exacerbate the effect of wildfire should it occur. The combination of a lack of resources and high risk from natural disasters is called Social Vulnerability.

Socially vulnerable communities are exposed disproportionally to fire and less covered by government mitigation programs. Gaither et al. (2011) find that, in the South, areas of high fire risk and high social vulnerability were less likely to participate in mitigation programs such as Firewise or Community Wildfire Protection Program (CWPP). Palaiologou et al. (2019) found that “Although high social vulnerability block groups covered small areas, they had high population and structure density and were disproportionately exposed per area burned by fire.”

Methods

The objective of this project is to construct a measure of fewer economic resources for the WUI in Colorado. The layer is intended to highlight areas that are eligible for a reduced match via the Colorado State Forest service grants for wildfire risk mitigation. The Index is calculated only for areas that contain WUI.

Map Details

The map above shows the CBGs in Colorado. All inhabited CBGs colored by their eligibility for increased match. CBGS that are colored red are eligible for increased match. This means that they have a WFSVI score of 75 or greater. If you click on a CBG a table will pop up and display the information that was used to calculate the SVI as well as the WFSVI ranking. On the map, areas without an WFSVI ranking due to their non-WUI status are marked Non-WUI, but the information is available.

Introduction to Index

We adapt the Social Vulnerability Index (SVI) (Flanagan et al., 2011) to the wildland fire context in Colorado. The SVI was created in response to Hurricane Katrina to capture the social (rather than biophysical) features of a community that make it vulnerable. The SVI ranges from 0 (not vulnerable) to 100 (highly vulnerable). It acknowledges that those who are socially vulnerable are more likely to experience loss and less likely to recover. Its original intent was to help state and federal level disaster responders identify the most affected areas post-event. The authors also recognize its potential for use in other stages of the disaster cycle, such as mitigation. Allocating funding for mitigation activities represents a forward-looking approach to equitable planning for wildfire incidents.

The projects funded by FRWRM fall into the mitigation and preparedness portions of the disaster cycle, but many of the post-event considerations apply to mitigation and preparedness. The SVI addresses four main categories of vulnerability: socioeconomic status, household composition/ disability, minority status/ language, and housing/transportation. These characteristics are important during all phases of the disaster cycle but especially mitigation. Those with little purchasing power, high burden of care, or who experience difficulty interacting with the community for cultural or physical reasons will be less able to participate in mitigation activity. Communities who have high overall levels of vulnerability may require additional assistance to complete migitation activities.

Average measures within a community may obscure the variation in vulnerability within the community. For example, a census defined geography may contain households that qualify as highly vulnerable and households that qualify as minimally vulnerable. Average characteristics reported in this community may indicate moderate vulnerability. We account for these heterogeneities by adding two measures to the WFSVI: income GINI and an education GINI. The GINI coefficient measures inequality, another measure of heterogeneity. Using the example of income, if everyone had the same income, the income GINI would equal zero. Conversely, if one household earned all the income in a CBG and the rest of the community received none, the income GINI would equal one.

We construct the WFSVI based on Flanagan et al (2011). The SVI and WFSVI are bounded between 0 and 1 with 0 being the least vulnerable. The following steps outline the construction of the WFSVI. We make a few minor changes to the original index variables to reflect modern ideas on measures of central tendency as well as data limitations. These changes are italicized.

  1. We percent rank the variables independently across all Census Block Groups (CBGs) in Colorado. We add the GINI variables to account for mixing of high and low levels of resources.

  2. We set weights of non-wildland fire pertinent variables such variable 13 in the list below. We also reweight other variables to emphasize resources that limit the ability to engage in mitigation. The reweights used are given below as percent contributions to the overall WFSVI.

  3. Finally we add them together, and percent rank them again. We place more weight on income and housing characteristics that are more relevant to wildfire risk. This percent rank is the WFSVI score.

  4. We can now assing the status of “fewer economic resources” with the WFSVI. We assign qualifying status to any CBG with a rank above .75. CBGs that rank in the top quartile in the WFSVI are eligible for an increased match.

Data

We construct the Colorado SVI using data from the 2016 American Community Survey (5-year average). The original SVI was calculated at the census tract, but the data are now available at the census block group (smaller geographic unit) to better identify community demographics. We then crop the spatial layers to the boundary of the WUI as defined by the boundaries used in the grant process.

We conduct all data analysis and geoprocessing in the R Statistical Computing Language. The code is made available on GitHub.

Variables

We describe the variables used to construct the Index below. We express the relative importance of the variables in the percentages in parentheses. The percentages represent the relative weight of the variable in the SVI ranking. The individual variable weights can be summed to represent the relative weight of the categories. The category weights are shown for reference.

Socioeconomic Status Variables (39 %)

  1. Percent of individuals below the poverty line (12.20 %)
  2. Percent of the Civilian labor force which is unemployed (7.31 %)
  3. Median household income (12.20 %)
  4. Percent of adults over 25 without a High School Diploma(7.31 %)

Household Composition/Disability (12.2 %)

  1. Percent of people over the age of 65 (2.44 %)
  2. Percent of people under the age of 18 (2.44 %)
  3. Percent of the poverty status determined population above 18 with a disability (2.44 %) (The original index included those over 5, but this data is not included in the ACS)
  4. Percent of households with a male or female householder, no spouse present, with children under 18 (2.44 %)

Minority Status/Language (17.1 %)

  1. Percent of the population that are of minority status (Non-white or Hispanic) (12.20 %)
  2. Percent of the population that speaks English less than “well” (4.88 %)

Housing/Transportation (7.32 %)

  1. Percent of housing structures that have 10 or more units (0%)
  2. Percent of housing units that are mobile homes (4.88 %)
  3. Percent of households that live in a housing unit with more people than rooms (0%)
  4. Percent of households with no vehicle (2.44%)
  5. Percent of people who live in group housing such as nursing homes (0%)

New Equity Variables (24.4 %)

  1. Income GINI (12.20 %)
  2. Years of schooling/ Education GINI (12.20 %)

References

Flanagan, Barry E., Edward W. Gregory, Elaine J Hallisey, Janet L. Heitgerd, and Brian Lewis. 2011. “A Social Vulnerability Index for Disaster Management.” Journal of Homeland Security and Emergency Management 8 (1). https://doi.org/10.2202/1547-7355.1792.

Gaither, Cassandra Johnson, Neelam C. Poudyal, Scott Goodrick, J. M. Bowker, Sparkle Malone, and Jianbang Gan. 2011. “Wildland Fire Risk and Social Vulnerability in the Southeastern United States: An Exploratory Spatial Data Analysis Approach.” Forest Policy and Economics 13 (1): 24–36. https://doi.org/10.1016/j.forpol.2010.07.009.

Palaiologou, Palaiologos, Alan A. Ager, Max Nielsen-Pincus, Cody R. Evers, and Michelle A. Day. 2019. “Social Vulnerability to Large Wildfires in the Western USA.” Landscape and Urban Planning 189 (September): 99–116. https://doi.org/10.1016/j.landurbplan.2019.04.006.